Unsolved Problems and Issues Related to the SLEUTH Urban Growth and Land Use Change Model Keith C. Clarke University of California, Santa Barbara SLEUTH Symposium I: After 15 years with the SLEUTH model, what we've learned and where we're headed

What I will cover Application and calibration problems Inherent assumptions that constrain the model forecasts Unsolved problems and issues surrounding SLEUTH and its use Opportunities for improving the model, or lessons learned for the next generation of modelers. Gaps in modeling generally and with urban/land use systems in particular

Why model? 1.Models are windows through which to understand data 2.Models are the bridge between description and analysis, and explanation and prediction 3.Prediction is the bridge to intervention and policy

Characteristics of models Have inputs and outputs Simplify and reduce Relate system to basic behavior of phenomenon (process) Recreate structures (form) Generate predictions (forecasts) Propagate errors (Calibration, validation) Allow experiments

Inherent assumptions Constants as variables Boom and Bust Slope Role of Monte Carlo/Averaging Frequencies vs. Probabilities Uncertainty Need numbers!

Modeling Cities and Land Use Change Data = maps and statistics Need historical data, sources often GIS data, maps and images Data need consistency –In space: reference frame –In time: but collected at uneven intervals –In theme: Land use classes vs. time Then simulation is possible

Meta Issues Human physics Roles of policy, decision-making, agents, culture, demography, etc. Model coupling Adjustments to the exclusionsweights Growth, no decline Markovian assumption

Data problems Remote sensing and the 85% Fusion across LU schema Lack of consistency over time Still tedious, and still most of the problem! Data rarely revisited or shared, even with the Data Inventory

Calibration problems Much disagreement about how OSM an important contribution Time beat the computer problems Still too little validation and sensitivity testing More scope for model comparison

Tractability problems SLEUTH remains CPU intensive Improvements to road search reported in Jantz et al pSLEUTH Genetic algorithms

Scenario-based planning Development of the exclusion layer always hardest, based on subjective choice Improvements embedded policy more objectively Latest work also includes Multi-Criteria weighting Still need some theory behind scenario creation

SLEUTH lessons learned Open source works really well! Sensitivity testing essential –Units –Overfit –Landscape metrics –Temporal –LU aggregation –Resolution –Monte Carlo

SLEUTH lessons learned Toward optimal calibration Coupling is powerful Link models to scenarios Visualization important Need stronger links to urban evolutionary theory, policy, socioeconomics and demography

Funding NSF Urban Research Initiative NSF Doctoral Improvement Program Los Alamos National Laboratories USGS (Menlo Park) Santa Barbara Economic Community Project US EPA MAIA Project